Abstract
In this paper, the problem of finite-time stability for a class of fractional-order Cohen–Grossberg BAM neural networks with time delays is investigated. Using some inequality techniques, differential mean value theorem and contraction mapping principle, sufficient conditions are presented to ensure the finite-time stability of such fractional-order neural models. Finally, a numerical example and simulations are provided to demonstrate the effectiveness of the derived theoretical results.
| Original language | English |
|---|---|
| Pages (from-to) | 1309-1320 |
| Number of pages | 12 |
| Journal | Neural Computing and Applications |
| Volume | 29 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - Jun 1 2018 |
Keywords
- Banach contraction principle
- Cohen–Grossberg BAM neural networks
- Finite-time stability
- Fractional-order derivative
- Time delay
ASJC Scopus subject areas
- Software
- Artificial Intelligence
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